Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data

BMC Bioinformatics

Giulio Caravagna¹  Daniele Ramazzotti²  Marco Antoniotti³  Luca De Sano³  Alex Graudenzi³ 

[1] Centre for Evolution and Cancer, The Institute of Cancer Research;Department of Pathology, Stanford University;Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca;

关键词: Single-tumour evolution;    Single-cell sequencing;    Multi-region sequencing;    Mutational graphs;    Cancer evolution;    Tumour phylogeny;   

DOI  :  10.1186/s12859-019-2795-4

来源: DOAJ

【 摘 要 】

Abstract Background A large number of algorithms is being developed to reconstruct evolutionary models of individual tumours from genome sequencing data. Most methods can analyze multiple samples collected either through bulk multi-region sequencing experiments or the sequencing of individual cancer cells. However, rarely the same method can support both data types. Results We introduce TRaIT, a computational framework to infer mutational graphs that model the accumulation of multiple types of somatic alterations driving tumour evolution. Compared to other tools, TRaIT supports multi-region and single-cell sequencing data within the same statistical framework, and delivers expressive models that capture many complex evolutionary phenomena. TRaIT improves accuracy, robustness to data-specific errors and computational complexity compared to competing methods. Conclusions We show that the application of TRaIT to single-cell and multi-region cancer datasets can produce accurate and reliable models of single-tumour evolution, quantify the extent of intra-tumour heterogeneity and generate new testable experimental hypotheses.

【 授权许可】

Unknown